Project Abstract

Abstract
Landslides are natural hazards that pose significant risks to human lives, infrastructure, and the environment. Understanding and analyzing landslide susceptibility is crucial for effective risk management and mitigation strategies. This research project focuses on the analysis of landslide susceptibility using Geographic Information System (GIS) and Remote Sensing techniques. The study begins with an introduction (Chapter 1) that provides background information on landslides, the problem statement, objectives of the study, limitations, scope, significance, structure of the research, and definitions of key terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to landslide susceptibility, GIS, Remote Sensing, and previous research findings in the field. Chapter 3 outlines the research methodology, including data collection techniques, GIS and Remote Sensing tools, statistical analyses, and modeling approaches. The chapter also discusses the selection of study areas, variables, and parameters used in the analysis. Chapter 4 presents the detailed discussion of findings, including the results of the landslide susceptibility analysis, spatial distribution patterns, correlation analysis, and the effectiveness of GIS and Remote Sensing techniques in predicting landslide occurrences. The chapter also includes a comparison of different modeling approaches and their implications for landslide risk assessment. In Chapter 5, the research concludes with a summary of key findings, implications for landslide risk management, limitations of the study, and recommendations for future research. The study highlights the importance of integrating GIS and Remote Sensing techniques for accurate landslide susceptibility mapping and risk assessment. The findings of this research contribute to the existing body of knowledge on landslide susceptibility analysis and provide valuable insights for policymakers, planners, and stakeholders involved in disaster risk reduction and land use planning.

Project Overview